code: def test_rnn_separator_forward_backward_real(
      input_dim, rnn_type, layer, unit, dropout, num_spk, nonlinear
  ):
      model = RNNSeparator(
          input_dim=input_dim,
          rnn_type=rnn_type,
          layer=layer,
          unit=unit,
          dropout=dropout,
          num_spk=num_spk,
          nonlinear=nonlinear,
      )
      model.train()
  
      x = torch.rand(2, 10, input_dim)
      x_lens = torch.tensor([10, 8], dtype=torch.long)
  
      masked, flens, others = model(x, ilens=x_lens)
  
      assert isinstance(masked[0], Tensor)
      assert len(masked) == num_spk
  
      masked[0].abs().mean().backward()
  
file_path: espnet/test/espnet2/enh/separator/test_rnn_separator.py
imports: import pytest
  import torch
  from torch import Tensor
  from torch_complex import ComplexTensor
  from espnet2.enh.separator.rnn_separator import RNNSeparator
label_desc: Use image node_id 2 for calling a global function with example usage: test_rnn_separator_forward_backward_real(input_dim, rnn_type, layer, unit, dropout, num_spk, nonlinear) without return types